Limited by the size of GPU memory, an entire simulation field (\(512^3\) ) cannot be fed at once to the network and has to be cropped into smaller chunks of size \(128^3\) . To preserve the translational equivariance, we keep no paddings in the convolutions, resulting in smaller network outputs than their inputs, and compensate this by periodically padding 48 voxels per side to the input fields. We train with a batch size of 16 distributed on 16 Nvidia V100 GPUs, and use the Adam optimizer [1]} with learning rate \(0.0004\) , and hyperparameters \(\beta _1 = 0.9\) , \(\beta _2 = 0.999\) . We reduce the learning to 10% when the loss does not improve for 3 epochs.
